Our solution for integrating a Remote Monitoring Application into Electronic Health Record (EHR) workflows significantly reduced physicians’ administrative burdens while enhancing data portability.
We designed and implemented a Self-Measured Blood Pressure Monitoring (SMBP) system using FHIR and EHR integration tools for seamless connectivity. The solution features advanced algorithms to calculate daily and weekly scores and averages, providing actionable insights for physicians.
The application also includes Patient Health Coaching services and seamless device integration, improving hypertension management and patient outcomes. This comprehensive approach has enhanced physician satisfaction, improved patient outcomes, and streamlined hypertension management.
Struggling with manual tracking and delayed insights? Learn how our tailored solutions helped a global beverage brand:
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Discover how to optimize deep link automation testing for OTT apps with scalable frameworks, dynamic data, and real-time insights. This whitepaper unveils strategies to enhance app navigation, improve testing efficiency, and drive user satisfaction. Learn how you can stay competitive in the OTT landscape with deep link testing.
Over-The-Top (OTT) applications have revolutionized content consumption by offering easy access to a wide array of content across multiple platforms. However, as the OTT landscape evolves, content fragmentation and churn rates increase. To address these, aggregators need to adopt deep linking to enhance user engagement and drive customer retention.
This whitepaper sheds light on the complexity of automating deep link testing for OTT applications, highlighting its transformative potential and detailing their role in user analytics, strategic partnerships, and precision marketing.
Reduced document analysis time by 60% with AI-powered summarization
Transformed manual document processing with AWS Bedrock’s Titan Text G1-Premier model
Streamlined analysis of multiple document formats through unified S3 storage system
Implemented secure, automated workflow from document ingestion to final report generation
Enhanced consistency in blanket position request assessments through standardized outputs
Discover how a leading passenger transportation company overcame challenges with outdated technology, high maintenance costs, and the need for a multitenant architecture to reduce costs for smaller customers. By implementing SaaS solutions, the company achieved significant operational cost savings and improved customer satisfaction.
Streamline your paratransit operations with our SaaS approach!
Managing different versions of fleet management software proved to be a daunting task. Each version came with its own set of features, bug fixes, and compatibility issues, leading to increased maintenance costs and longer downtimes.
To address these challenges, we embarked on a comprehensive re-architecting of the scheduling and dispatching application. The application was redeveloped using .NET Core API and deployed in the Azure App Services environment, ensuring better performance and scalability.
The implementation of advanced SaaS solutions led to remarkable business outcomes. The company achieved up to 55% operational cost savings through optimized database access and multitenancy.
Achieved a 70% increase in productivity while reducing operational costs by 40%
Enhanced fraud detection accuracy by 80%, with alert coverage rising from 60% to 95%
Reduced alert backlog by 85%, maintaining a system uptime of 99.9%
Unified real-time risk assessment across three banking platforms
Automated processes for alert consolidation, case monitoring, and SAR tracking
Dscover how a leading consumer goods manufacturer achieved 50% faster raw material delivery to their production lines.
Struggling with delays and bottlenecks in your production process? Learn how our tailored solutions helped a global manufacturer:
By optimizing raw material transport, they achieved measurable results in efficiency and production timelines.
A Case Study on Harnessing Technology to Enhance Service Delivery and Customer Experience
Client Overview:
The client, a leading passenger transportation company, provides intelligent transport systems and software solutions for the public transport sector as well as for demand response and special student transport.
Challenge/ Business Need:
Solution Provided R Systems:
Outcome/ Results:
Technology Stack
Conclusion:
This case study highlights the successful modernization and migration of the client’s products to a SaaS platform, resulting in significant cost savings and improved efficiency.
Call to Action:
Streamline your paratransit operations with our SaaS approach. Contact us now to get started.
Author: Razvan Rusu
Gen AI is a very powerful tool that simplifies complex tasks in many areas including the technology field. This article tries to answer the question: Can Gen AI reduce the complexity of testing in telecom?
The short answer is Yes, in multiple ways, but AI won’t do all the work for us.
Mobile telephony is an easy-to-use service with a lot of complexity behind the scenes. Making a phone call is trivial, but this simple operation involves numerous systems and dozens of messages being exchanged. From the initial device authorization to the call end, all these messages are needed.
There are a few reasons why there are so many systems and messages involved:
The communication takes place over an unsecured medium (wireless). Authorization and setting of the encryption keys must be performed before any call/data session. Encryption makes sure nobody can listen to your conversation or see your data transfer. Authorization, on the other hand, makes sure your phone can’t be cloned, which would allow another malicious device to receive or make calls as if it were your phone.
The standardization for GSM is done by 3GPP (https://www.3gpp.org/about-us). The main driver for this standardization is interoperability between operators and interoperability between various vendors. An NE, (Network Element) part of the GSM core network, will work the same way for an operator in the United States as for an operator in Indonesia.
This standardization has some obvious advantages (roaming, for instance—a service we couldn’t live without these days), but it also has some drawbacks. The architecture was split into multiple systems (Network Elements) with clearly defined functionality and message flows. All mobile operators must use these Network Elements in the same way. None of them can decide they don’t like how things are working and choose to handle calls differently, like for instance having a single system performing all the logic. Everyone must stick to what the standard specifies.
We can make calls on a 2G/3G connection or over a 4G/5G connection, depending on the coverage provided by the mobile operator in the area where we are located. The type of connectivity used is not within our control, and we expect consistent behavior for our calls. For instance, we expect to be informed if the called party has been ported to another mobile operator and we expect to be charged the same way regardless of the connection used for making that call.
Even more, a call may start on a 4G coverage as a VoLTE call and continue as a 3G call once the 4G coverage is lost. The caller shouldn’t feel this transition as for him it is the same call. However, for the mobile operator, switching from 4G to 3G is a big change that involves multiple systems and messages.
Testing a mobile service is as easy as making and answering a phone call. Or so it seems.
Testing using mobile phones has a few advantages:
This testing method appears to be simple and very effective. Therefore it has been adopted by many mobile operators. Even more, this testing method was automated. Either with specialized equipment or by remotely controlling mobile phones. There are many solutions available for this type of automation.
If this method is effective, automated, and end-to-end, what more could be required? Well, let’s take a closer look at what this method does not cover. First of all, it checks only the edges of the solution. Did we notify all the systems that should have been notified about that call? We can’t say because this is not part of the test.
To make a parallel with testing an online shop: testing if the Place Order function works properly is done solely on the result page seen by the user. Whether the warehouse or the invoicing system was notified about that order is not checked. This would be unacceptable for testing an online shop. So why is it acceptable for mobile operators? We’ll discuss this a bit later.
The second big drawback of this mobile phone testing method is the limitation imposed by the device used for these tests. Several types of tests can’t be executed:
A new question arises: With all these problems, what makes this testing method so widely adopted? The answer lies in the complexity of the systems involved and the difficulty of having a test team with the required specialized technical knowledge. When running acceptance tests for Network Elements, mobile operators rely on the supplier of that NE. The supplier’s engineers possess the deep technical knowledge, and the mobile operator typically only observes and validates the process, without performing any actual testing themselves.
At the same time, mobile operators focus on testing new functionalities, such as a new voice plan, or a new data offering (e.g. free access to Instagram and TikTok). Regression testing is only seen as a nice-to-have.
There isn’t a simple solution. If one existed, it would have been already used by mobile operators. However, this doesn’t mean there is no solution. Since it’s a complex problem, the best approach is to split it. Isolate the complex technical parts from the business-driven parts.
The technical parts hardly ever change in terms of the systems involved and message flows. It must be compliant with the 3GPP standards. So there isn’t a lot of room for creativity. What changes from test to test are the attributes/parameters of the messages. If you have a parametrized module that sends the messages and validates the responses, all you need do is call that module with the right parameter values. You don’t need to know the protocols involved or the specific messages that will be exchanged; the module will handle this complexity for you. This allows the QA team to run proper and complete testing without requiring deep technical knowledge.
For instance, let’s consider the example above. There is a new voice plan where calls are being charged differently. When placing a call, a CAP session triggers a Diameter Ro session towards OCS for 2G calls, or an SIP session which triggers a Diameter session for VoLTE (4G) calls. If you have a module that receives as parameters the originating party (A#), the calling party (B#), and the duration of the call, the QA team doesn’t need to know CAP, SIP, or Diameter, even though the test suite makes use of these protocols.
This separation allows the QA team to focus on testing functionality while simulating and validating the flows and data exchanged at telco-specific protocols. Testing becomes a bit more complicated than making a phone call, but not significantly so. The modules need to be called with the right parameters and their output needs to be validated. This can be done by an orchestrator (for instance a Shell/Python script) that takes input text files in CSV format and outputs the result in CSV format. The CSV format has several advantages:
Having the test data (input data and expected results) in files opens the door to automation. The test execution can be easily integrated into a CI/CD pipeline. However, there is one additional thing to be considered before declaring the tests automated. The test scenarios need to be executed repeatedly and produce consistent results. They must be idempotent and repeatable to be added to an automated test suite. The steps of an idempotent test are:
The success of Generative AI created a lot of hype. Enterprises are increasingly adopting Gen AI across their organizations. Chat GPT and GitHub Copilot have proven able to generate pieces of code and have become very useful tools for software developers.
Can Gen AI be used effectively in testing? Certainly, it can, and there are 2 main areas where it can help. (Note: the use cases presented below are not theoretical; they have been successfully implemented.)
This is considered the Holy Grail of Gen AI in testing – take as input a test plan, or even better the specification document, and generate the test suite. While Gen AI is not yet at this point, just as in the case of software development it can be used by QA engineers to develop faster test cases. The complexity isolation described above is very useful when generating test cases with AI.
Expecting Gen AI to generate the right messages, in the right order and with the right parameters according to 3GPP is unrealistic. And even if it could, the benefit would be limited as new business requirements don’t modify the 3GPP specifications. However, asking Gen AI to generate CSV files in a specific format with data presented in a natural language is a realistic expectation. For instance, you can give the following prompt to Gen AI: “Verify that a national call of 5 minutes deducts 300 units from NationalSeconds balance” or “A call of 2 minutes to +49123456789 should charge 0.012 EUR from the monetary balance”.
With some clever prompt engineering, Gen AI will generate CSV lines in the right format. This allows the QA team to focus on what they want to test rather than how the test is going to be conducted. Another benefit is significantly reducing the ramp-up effort required for new team members.
There are situations where it’s crucial to understand the specific details of what went wrong in a test case, especially during regression testing. Most likely, something is wrong, preventing the new release from being deployed into production. But we must also investigate the issue.
If the problem is related to the business logic introduced by the new release, it may be easier to identify the cause. On the other hand, issues related to telco-specific protocols used during regression testing pose greater challenges, especially when the QA team lacks deep knowledge of these protocols.
Another scenario where detailed telco understanding is crucial is when developing telco-specific modules. If the QA engineer writes a test that fails, is the failure a test problem or an application problem? The 3GPP standard and the application specifications should provide clarity in such cases. However, in practice, this isn’t always the case. Have you ever tried to read a 3GPP document? To put it mildly, it’s not the most easily readable documentation. The complexity arises because each document references another, which references another, and so on. This complexity, while justified by the technical intricacies of telco standards, can be daunting for newcomers to the field.
Besides the standards and the project/system-specific documentation, another important source of information for the QA team is the history of tickets previously reported for that project/system. Since, in the telco world, a system is used for many years (often more than 10), these tickets provide valuable information. However, the sheer volume of tickets can be overwhelming, making it difficult, if not impossible, for a QA engineer to determine if a current problem has been previously reported. As a result, new tickets are frequently created, further increasing the number of tickets and decreasing the likelihood of identifying similar or identical issues.
Gen AI proves to be very useful for this problem. All we need is to create a custom knowledge base that includes:
This way, Gen AI can quickly provide relevant information about a particular situation, indicating which parts of the documents are applicable. This saves hours or even days of digging through standards. Identifying existing tickets similar to the current failure is also extremely valuable, as these tickets include details on how the problem was solved, which might be applicable to the current situation.
Asking the questions in a natural language makes the adoption of such a solution instantaneous.
Even though using Gen AI in testing is not yet mainstream, it has already been proven to facilitate the testing process. Thus, I anticipate a gradual but continuous adoption of Gen AI in testing overall, and specifically in telecom testing.
Many organizations place a strong focus on collecting as much data as possible. However, being data-rich is not the same as being insight-rich. While collecting data is important, analyzing it to gain insights is invaluable to maintaining the competitive edge and long-term business success.
Armed with insights, organizations can get quantitative and qualitative answers to business-critical questions that enable sound decision-making with number-driven rationale.
Continuous and sustained business success depends on how quickly and strategically organizations can convert their data into insights, then put them into action. If you aren’t able to leverage insights-to-action, the following five factors might be your culprits:
Insight-driven organizations don’t just gather data, they put it to use to create better products, design more effective strategies, and engender a superior customer experience.
In a nutshell, “Data Democratization” refers to hindrance-free, easy access to data for everyone within an organization. Further, all stakeholders should be able to understand this data to expedite decision-making and unearth opportunities for quicker growth.
The distribution of information through Data Democratization enables teams within an organization to gain a competitive advantage by identifying and acting on critical business insights. It also empowers stakeholders at all levels to be accountable for making data-backed decisions.
Concerns that commonly keep organizations from democratizing data include; poor handling and misinterpretation by non-technical teams, which can lead to inept decision-making.
Additionally, with more people having access to business-critical data, the question of maintaining data security and data integrity cannot be ignored. Another concern relates to cleaning up inconsistencies – even in the smallest datasets and files. These may need to be converted into different formats before they can be used.
However, technical innovations – such as cloud storage, software for data visualization, data federation, and self-service BI applications – can make it easy for non-technical people to analyze and interpret data correctly.
Data Democratization is expected to give rise to new business models, help uncover untapped opportunities, and transform the way businesses make data-driven decisions. You don’t want to overlook this!
With organizations using the multichannel customer service approach, customers have the option of using a number of two-way channels to communicate with brands. These typically include email, phone, live chat, social media, online forms, and so on. It, therefore, becomes difficult for customer service teams to unify customer data received from these sources for analysis and interpretation.
SCV enables organizations to track customers and their messages across channels, which in turn, helps with:
United Airlines, upon merging with Continental Airlines in 2012, wanted to integrate the two companies’ websites. United also wanted to ensure that its analytics and marketing pixel tagging was accurate, and ultimately, work towards a single customer view across all channels. They unified tagging across all digital touchpoints, including mobile apps and kiosks.
United managed to combine all customer data, which left them with cleaner datasets, greater consistency across applications, and the elimination of inefficient data silos. They also achieved higher ROI, as well as enhanced analytics and optimization programs that unified customer data and enabled greater mobile marketing agility.
Frequent technological advancements and industry disruptions have necessitated digital transformation in organizations. This, in turn, has given rise to new opportunities for growth and exchange of innovative ideas that transcend the borders of the R&D department.
If organizations are to encourage an enterprise-wide culture of innovation, they need to redefine metrics and incentives accordingly. New ventures and initiatives cannot be evaluated with traditional metrics to measure success.
Most managers agree that taking calculated risk is crucial to innovation, but putting this thought into practice is easier said than done. Hence, the focus needs to be on encouraging teams to take smart risks. It helps to clearly define a “smart risk” for teams and departments to distinguish the areas where risk is encouraged (and where it isn’t).
Of course, taking smart risks in business involves using advanced data analytics, Internet of Things, images, annotations, RFID, telematics, audits, among others. Every team brings unique perspectives to the table, which can provide ideas and insights to solve business problems. These insights are at the heart of driving successful innovation.
If your data is in multiple silos, gaining actionable insights from it can be a mammoth task for your organization. More often than not, the lack of customer insight is the result of the inability to consolidate customer information across channels.
The biggest challenge here is the inconsistent collection of customer information in each channel. For example, a global hotel brand may have collected customer data in a bid to improve customer service. However, because the data was collected from various sources, it resulted in some serious inaccuracies and inconsistencies.
However, after consolidating each customer’s data in one place, hotel staff can provide them with enhanced services and experiences across properties. Staff can guide a yoga-aficionado guest with a list of local studios and class times; or simply stock the mini-bar with their guest’s preferred beverages. Such steps will result in improved customer satisfaction and increased customer lifetime value.
Challenges related to data consolidation can be mitigated by enhancing data collection methods, in terms of accuracy and consistency. This also applies to how and where the information is stored upon being collected.
Organizations will do well to use cloud-based data consolidation tools. These tools are especially designed to provide speed, security, scalability, and flexibility, regardless of the place or in the form in which your data exists. These systems ensure that complete and accurate datasets are available at your disposal at anytime from anywhere.
Modern organizations use multiple channels to connect with and engage customers, but struggle to derive actionable insights from all the available data. It is necessary that organizations gauge quantitative and qualitative data to arrive at measurable and countable answers, which can be converted into numbers and statistical data.
This, in turn, will help decipher customer motivations, indicate their preferences, and highlight the scope for improvement.
Advanced technologies – such as Artificial Intelligence, Machine Learning, Augmented Reality, and Blockchain – are being leveraged to engage customers and provide them with seamless, connected, and hassle-free experiences. These solutions can also measure customer satisfaction using quantitative and qualitative data, which can be gathered through questionnaires and surveys. Combining survey answers and hard data will present the most direct picture of customers’ experiences.
The most crucial elements of success with customer experiences when implementing these technologies are: putting data at the center of your customer experience and seamlessly merging the digital and the physical (i.e. merging data from in-store and online experiences).
It also helps to use data analytics to find meaningful success metrics like revenue per visit, average user duration/average user time on site, cost per acquisition (CPA), and cost per lead (CPL) for gaining real-time feedback. Looking through CRM and lead platforms and working out total conversions for a particular time period can prove helpful.
Once these aspects are taken care of, organizations should be able to find answers to their most burning questions.
Business Analytics helps collect and analyze historical data, then employs predictive analytics and generates visual reports in custom dashboards. Predictive modeling can forecast and prepare businesses for future requirements/obstacles.
Organizations can begin using business analytics by asking measurable, clear, and concise questions. This should be followed by setting realistic measurement priorities, and then collecting and organizing data. The next steps involve the analysis of trends, parallels, disparities, outliers, and finally, interpretation of results.
The primary advantage of harnessing Business Analytics is to decipher patterns in data to gain faster and more accurate insights. Doing so enables organizations to track and act immediately, as well as formulate better and more efficient strategies to drive desired business and customer outcomes.
In any organization, Data Analytics should not be the forte of only data analysts and data scientists. Other stakeholders must also be empowered to make sense of critical data. Proper, user-friendly Data Visualization is the answer when organizations want to process and translate large volumes of datasets into meaningful insights.
Organizations must realize that there is more to Data Visualization than displaying information in a particular format. It also enables the use of visual instructions that guide users to process the material easily, with business-critical insights prominently featured on the top of the visual hierarchy.
Data Visualization also empowers organizations to easily decipher hidden patterns and make sense of the bigger picture in the ocean of data. With more meaningful data at your disposal, you will see improved decision-making (and revenue growth), as well as customer satisfaction and failure-aversion strategies.
So, you need to make Data Visualization a key skill of all data scientists in your organization. The goal is to make every single insight and decision crystal clear for all stakeholders to absorb.
Traditionally, organizations resort to historical data, spreadsheets, and business tools to make sense of their data. However, with different variables coming into play and constraints to consider, doing so across multiple channels can become increasingly complex and error-prone.
By bringing AI into the mix, however, management of data has now become quicker and error-free. Organizations can easily analyze their performance across the value chain in real time. With AI-powered operations, businesses can predict elements such as risks and customer behavior, then devise strategies to improve performances and approaches.
AI makes it possible for data-driven organizations to compare performance and trends, as well as analyze every dataset to gain business insights. These can then be turned into actionable plans that enable businesses to optimize their approach to enhance ROI and better meet customer needs.
AI helps to close the gap between insight and action by increasing scale, speed, and efficiency. Organizations can close the gap by analyzing customer data to derive key information, plan how to implement it, then focus on key performance drivers. Once this is done, organizations must track the progress of their plan and manage risks. After this, the desired outcomes can be achieved.
Decision-making fueled by AI can be done proactively, as well as more efficiently and effectively. Business insights can be embedded into predictive models that enhance business outcomes way beyond what was thought possible with traditional approaches.
Traditionally, organizations resort to historical data, spreadsheets, and business tools to make sense of their data. However, with different variables coming into play and constraints to consider, doing so across multiple channels can become increasingly complex and error-prone.
By bringing AI into the mix, however, management of data has now become quicker and error-free. Organizations can easily analyze their performance across the value chain in real time. With AI-powered operations, businesses can predict elements such as risks and customer behavior, then devise strategies to improve performances and approaches.
AI makes it possible for data-driven organizations to compare performance and trends, as well as analyze every dataset to gain business insights. These can then be turned into actionable plans that enable businesses to optimize their approach to enhance ROI and better meet customer needs.
AI helps to close the gap between insight and action by increasing scale, speed, and efficiency. Organizations can close the gap by analyzing customer data to derive key information, plan how to implement it, then focus on key performance drivers. Once this is done, organizations must track the progress of their plan and manage risks. After this, the desired outcomes can be achieved.
Decision-making fueled by AI can be done proactively, as well as more efficiently and effectively. Business insights can be embedded into predictive models that enhance business outcomes way beyond what was thought possible with traditional approaches.
The process of transforming raw data into actionable insights can be daunting. However, doing so is crucial if you want to stay competent and remain ahead of the curve. To successfully lead data-driven initiatives, organizations must overcome the challenges of data accumulation, analysis, and action.
Integrating data sources and leveraging advanced technology for faster and more accurate analyses is imperative. The future belongs to organizations that are driven by data, and only the optimal extraction and application of insights can give rise to the finest business outcomes.
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